Methods and devices for prediction dependent residual scaling for video coding

ABSTRACT

Methods and devices are provided for reducing the decoding latency introduced by LMCS. In one method, one or more luma prediction sample values are selected from an output of a bilinear filter of Decoder-side Motion Vector Derivation (DMVR), the one or more selected luma prediction sample values are adjusted into luma prediction sample values with the same bit depth as an original coding bit depth of an input video, the luma prediction sample values with the same bit depth as the original coding bit depth of the input video are used to derive a scaling factor for decoding one or more chroma residual samples, the scaling factor is used to scale one or more chroma residual samples, and one or more chroma residual samples are reconstructed by adding the one or more scaled chroma residual samples and their corresponding chroma prediction samples.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.:PCT/US2020/038994, filed on Jun. 22, 2020, which is based upon andclaims the benefit to U.S. provisional patent application Ser. No.62/865,142, filed on Jun. 21, 2019, the disclosures of which areincorporated herein by reference in their entireties herein by referencefor all purposes.

FIELD

The present disclosure relates generally to video coding andcompression. More specifically, this disclosure relates to systems andmethods for performing video coding using prediction dependent residualscaling on code units.

BACKGROUND

This section provides background information related to the presentdisclosure. The information contained within this section should notnecessarily be construed as prior art.

Any of various video coding techniques may be used to compress videodata. Video coding can be performed according to one or more videocoding standards. Some illustrative video coding standards includeversatile video coding (VVC), joint exploration test model (JEM) coding,high-efficiency video coding (H.265/HEVC), advanced video coding(H.264/AVC), and moving picture experts group (MPEG) coding.

Video coding generally utilizes predictive methods (e.g.,inter-prediction, intra-prediction, or the like) that take advantage ofredundancy inherent in video images or sequences. One goal of videocoding techniques is to compress video data into a form that uses alower bit rate, while avoiding or minimizing degradations to videoquality.

The first version of the HEVC standard was finalized in October 2013,which offers approximately 50% bit-rate saving or equivalent perceptualquality compared to the prior generation video coding standardH.264/MPEG AVC. Although the HEVC standard provides significant codingimprovements than its predecessor, there is evidence that superiorcoding efficiency can be achieved with additional coding tools overHEVC. Based on that, both VCEG and MPEG started the exploration work ofnew coding technologies for future video coding standardization. oneJoint Video Exploration Team (JVET) was formed in October 2015 by ITU-TVECG and ISO/IEC MPEG to begin significant study of advancedtechnologies that could enable substantial enhancement of codingefficiency. One reference software called joint exploration model (JEM)was maintained by the JVET by integrating several additional codingtools on top of the HEVC test model (HM).

In October 2017, the joint call for proposals (CfP) on video compressionwith capability beyond HEVC was issued by ITU-T and ISO/IEC. In April2018, 23 CfP responses were received and evaluated at the 10-th JVETmeeting, which demonstrated compression efficiency gain over the HEVCaround 40%. Based on such evaluation results, the JVET launched a newproject to develop the new generation video coding standard that isnamed as Versatile Video Coding (VVC). In the same month one referencesoftware, called VVC test model (VTM), was established for demonstratinga reference implementation of the VVC standard.

Predictive methods utilized in video coding typically include performingspatial (intra frame) prediction and/or temporal (inter frame)prediction to reduce or remove redundancy inherent in the video data,and are typically associated with block-based video coding. Like HEVC,the VVC is built upon the block-based hybrid video coding framework

In block-based video coding, the input video signal is processed blockby block. For each block (also known as a coding unit (CU)), spatialprediction and/or temporal prediction may be performed. In newer videocoding standards such as the now-current VVC design, blocks may befurther partitioned based on a multi-type tree structure that includesnot only quad-trees, but also binary and/or ternary-trees. This allowsbetter accommodation of varying local characteristics.

Spatial prediction (also known as “intra prediction”) uses pixels fromthe samples of already coded neighboring blocks (which are calledreference samples) in the same video picture/slice to predict thecurrent block. Spatial prediction reduces spatial redundancy inherent inthe video signal.

During the decoding process, the video bit-stream is first entropydecoded at entropy decoding unit. The coding mode and predictioninformation are sent to either the spatial prediction unit (when intracoded) or the temporal prediction unit (when inter coded) to form theprediction block. The residual transform coefficients are sent toinverse quantization unit and inverse transform unit to reconstruct theresidual block. The prediction block and the residual block are thenadded together. The reconstructed block may further go through in-loopfiltering before it is stored in reference picture store. Thereconstructed video in reference picture store is then sent out to drivea display device, as well as used to predict future video blocks.

In newer video coding standards such as the now-current VVC design, thecoding tool of luma mapping with chroma scaling (LMCS) may be appliedbefore in-loop filtering. LMCS aims at adjusting the dynamic range ofthe input signal to improve the coding efficiency.

However, the now-current design of the LMCS incurs extra requirement ofcomputation complexity and on-chip memory because it uses differentdomain mappings at various decoding modules. Moreover, the now-currentdesign of the LMCS uses different luma prediction sample values toderive luma and chroma scaling factors, which introduces extracomplexity. Furthermore, the now-current design of the LMCS increasesthe latency of reconstruction of chroma residual samples because itrequires the postponement of reconstruction of chroma residual samplesuntil after the successful completion of luma prediction samples, whichin turn requires the successful completion of the sequentialapplications of complex inter mode coding tools such as Decoder-sideMotion Vector Derivation (DMVR), Bi-Directional Optical Flow (BDOF) andCombined Inter and Intra Prediction (CIIP).

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

According to a first aspect of the present disclosure, a video codingmethod is performed at a computing device having one or more processorsand memory storing a plurality of programs to be executed by the one ormore processors. The method includes obtaining a luma prediction samplefor decoding a luma residual sample. The method further includesderiving a scaling factor using the luma prediction sample. The methodfurther includes using the scaling factor to scale the luma residualsample. The method further includes calculating a reconstructed lumasample by adding the luma prediction sample and the scaled luma residualsample.

According to a second aspect of the present disclosure, a video codingmethod is performed at a computing device having one or more processorsand memory storing a plurality of programs to be executed by the one ormore processors. The method includes obtaining a luma prediction samplevalue for decoding both a luma residual sample and a chroma residualsample at an input position. The method further includes obtaining aluma prediction sample associated with the luma residual sample. Themethod further includes obtaining a chroma prediction sample associatedwith the chroma residual sample. The method further includes using theluma prediction sample to derive a first scaling factor for the lumaresidual sample and a second scaling factor for the chroma residualsample. The method further includes using the first scaling factor toscale the luma residual sample. The method further includes using thesecond scaling factor to scale the chroma residual sample. The methodfurther includes calculating a reconstructed luma sample by adding theluma prediction sample and the scaled luma residual sample. The methodfurther includes calculating a reconstructed chroma sample by adding thechroma prediction sample and the scaled chroma residual sample.

According to a third aspect of the present disclosure, a video codingmethod is performed at a computing device having one or more processorsand memory storing a plurality of programs to be executed by the one ormore processors. The method includes obtaining a plurality of lumaprediction samples by skipping a number of a pre-defined intermediateluma prediction stages during a luma prediction process for a codingunit (CU). The method further includes using the obtained plurality ofluma prediction samples to derive scaling factors for chroma residualsamples in the CU. The method further includes using the scaling factorsto scale the chroma residual samples in the CU. The method furtherincludes calculating a reconstructed chroma sample by adding the chromaprediction samples and the scaled chroma residual samples in the CU.

According to a fourth aspect of the present application, one or moreluma prediction sample values are selected from an output of a bilinearfilter of Decoder-side Motion Vector Derivation (DMVR), the one or moreselected luma prediction sample values are adjusted into luma predictionsample values with the same bit depth as an original coding bit depth ofan input video, the luma prediction sample values with the same bitdepth as the original coding bit depth of the input video are used toderive a scaling factor for decoding one or more chroma residualsamples, the scaling factor is used to scale one or more chroma residualsamples, and one or more chroma residual samples are reconstructed byadding the one or more scaled chroma residual samples and theircorresponding chroma prediction samples.

According to a fifth aspect of the present application, one or more lumareference sample values are selected from reference pictures, the one ormore selected luma reference sample values are transformed into a lumasample value, the luma sample value is used to derive a scaling factor,the scaling factor is used to scale one or more chroma residual samples,and one or more chroma residual samples are reconstructed by adding theone or more scaled chroma residual samples and their correspondingchroma prediction samples.

According to a sixth aspect of the present application, a computingdevice includes one or more processors, memory and a plurality ofprograms stored in the memory. The programs, when executed by the one ormore processors, cause the computing device to perform operations asdescribed above in the first five aspects of the present application.

According to a seventh aspect of the present application, anon-transitory computer readable storage medium stores a plurality ofprograms for execution by a computing device having one or moreprocessors. The programs, when executed by the one or more processors,cause the computing device to perform operations as described above inthe first five aspects of the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

Hereinafter, sets of illustrative, non-limiting embodiments of thepresent disclosure will be described in conjunction with theaccompanying drawings. Variations of structure, method, or functionalitymay be implemented by those of ordinary skill in the relevant art basedon the examples presented herein, and such variations are all containedwithin the scope of the present disclosure. In cases where no conflictis present, the teachings of different embodiments may, but need not, becombined with one another.

FIG. 1 is a block diagram setting forth an illustrative block-basedhybrid video encoder which may be used in conjunction with many videocoding standards.

FIG. 2 is a block diagram setting forth an illustrative video decoderwhich may be used in conjunction with many video coding standards.

FIG. 3 is an illustration of block partitions in a multi-type treestructure which may be used in conjunction with many video codingstandards.

FIG. 4 is a flow chart illustrating the decoding process with LMCS beingapplied.

FIG. 5 is an illustration of the BDOF process.

FIG. 6 is a flow chart illustrating the workflow of the chroma residualscaling in LMCS when all of the DMVR, the BDOF and the CIIP are enabled.

FIG. 7 is a flow chart illustrating the steps of the first aspect of thepresent disclosure.

FIG. 8 is a flow chart illustrating the workflow of the decoding processwhen the first aspect of the present disclosure is applied in the LMCSprocess.

FIG. 9 is an illustration of the residual mapping error caused by merelyusing the prediction sample to derive the scaling factor.

FIG. 10 is a flow chart illustrating the steps of the second aspect ofthe present disclosure.

FIG. 11 is a flow chart illustrating the steps of the third aspect ofthe present disclosure.

FIG. 12 is a flow chart illustrating the workflow of the LMCS decodingprocess in one example of the third aspect of the present disclosurewhere the DMVR, the BDOF and the CIIP are not applied to generate theluma prediction samples for the chroma scaling.

FIG. 13 is a flow chart illustrating the workflow of the LMCS decodingprocess in a second example of the third aspect of the presentdisclosure where the initial uni-prediction signal is applied togenerate the luma prediction samples for the chroma scaling.

FIG. 14 is a flow chart illustrating the steps of the fourth aspect ofthe present disclosure.

FIG. 15 is a flow chart illustrating the workflow of the LMCS decodingprocess in one or more embodiments of the fourth aspect of the presentdisclosure.

FIG. 16 is a flow chart illustrating the workflow of the LMCS decodingprocess in one other embodiment of the fourth aspect of the presentdisclosure.

FIG. 17 is a flow chart illustrating the steps of the fifth aspect ofthe present disclosure.

DETAILED DESCRIPTION

The terms used in the present disclosure are directed to illustratingparticular examples, rather than to limit the present disclosure. Thesingular forms “a” “an” and “the” as used in the present disclosure aswell as the appended claims also refer to plural forms unless othermeanings are definitely contained in the context. It should beappreciated that the term “and/or” as used herein refers to any or allpossible combinations of one or more associated listed items.

It shall be understood that, although the terms “first,” “second,”“third,” etc. may be used herein to describe various information, theinformation should not be limited by these terms. These terms are onlyused to distinguish one category of information from another. Forexample, without departing from the scope of the present disclosure,first information may be termed as second information; and similarly,second information may also be termed as first information. As usedherein, the term “if” may be understood to mean “when” or “upon” or “inresponse to,” depending on the context.

Reference throughout this specification to “one embodiment,” “anembodiment,” “another embodiment,” or the like in the singular or pluralmeans that one or more particular features, structures, orcharacteristics described in connection with an embodiment are includedin at least one embodiment of the present disclosure. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment,”“in another embodiment,” or the like in the singular or plural invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics in one or more embodiments may becombined in any suitable manner.

Conceptually, many video coding standards are similar, including thosepreviously mentioned in the Background section. For example, virtuallyall video coding standards use block-based processing, and share similarvideo coding block diagrams to achieve video compression.

FIG. 1 shows a block diagram of an illustrative block-based hybrid videoencoder 100 which may be used in conjunction with many video codingstandards. In the encoder 100, a video frame is partitioned into aplurality of video blocks for processing. For each given video block, aprediction is formed based on either an inter prediction approach or anintra prediction approach. In inter prediction, one or more predictorsare formed through motion estimation and motion compensation, based onpixels from previously reconstructed frames. In intra prediction,predictors are formed based on reconstructed pixels in a current frame.Through mode decision, a best predictor may be chosen to predict acurrent block.

A prediction residual, representing the difference between a currentvideo block and its predictor, is sent to a Transform circuitry 102.Transform coefficients are then sent from the Transform circuitry 102 toa Quantization circuitry 104 for entropy reduction. Quantizedcoefficients are then fed to an Entropy Coding circuitry 106 to generatea compressed video bitstream. As shown in FIG. 1, prediction-relatedinformation 110 from an inter prediction circuitry and/or an IntraPrediction circuitry 112, such as video block partition info, motionvectors, reference picture index, and intra prediction mode, are alsofed through the Entropy Coding circuitry 106 and saved into a compressedvideo bitstream 114.

In the encoder 100, decoder-related circuitries are also needed in orderto reconstruct pixels for the purpose of prediction. First, a predictionresidual is reconstructed through an Inverse Quantization 116 and anInverse Transform circuitry 118. This reconstructed prediction residualis combined with a Block Predictor 120 to generate un-filteredreconstructed pixels for a current video block.

Temporal prediction (also referred to as “inter prediction” or “motioncompensated prediction”) uses reconstructed pixels from already-codedvideo pictures to predict the current video block. Temporal predictionreduces temporal redundancy inherent in the video signal. Temporalprediction signal for a given CU is usually signaled by one or moremotion vectors (MVs) which indicate the amount and the direction ofmotion between the current CU and its temporal reference. Also, ifmultiple reference pictures are supported, one reference picture indexis additionally sent, which is used to identify from which referencepicture in the reference picture store the temporal prediction signalcomes.

After spatial and/or temporal prediction is performed, an intra/intermode decision circuitry 121 in the encoder 100 chooses the bestprediction mode, for example based on the rate-distortion optimizationmethod. The block predictor 120 is then subtracted from the currentvideo block; and the resulting prediction residual is de-correlatedusing the transform circuitry 102 and the quantization circuitry 104.The resulting quantized residual coefficients are inverse quantized bythe inverse quantization circuitry 116 and inverse transformed by theinverse transform circuitry 118 to form the reconstructed residual,which is then added back to the prediction block to form thereconstructed signal of the CU. Further in-loop filtering 115, such as adeblocking filter, a sample adaptive offset (SAO), and/or an adaptivein-loop filter (ALF) may be applied on the reconstructed CU before it isput in the reference picture store of the picture buffer 117 and used tocode future video blocks. To form the output video bitstream 114, codingmode (inter or intra), prediction mode information, motion information,and quantized residual coefficients are all sent to the entropy codingunit 106 to be further compressed and packed to form the bit-stream.

For example, a deblocking filter is available in AVC, HEVC as well asthe now-current version of VVC. In HEVC, an additional in-loop filtercalled SAO (sample adaptive offset) is defined to further improve codingefficiency. In the now-current version of the VVC standard, yet anotherin-loop filter called ALF (adaptive loop filter) is being activelyinvestigated, and it has a good chance of being included in the finalstandard.

These in-loop filter operations are optional. Performing theseoperations helps to improve coding efficiency and visual quality. Theymay also be turned off as a decision rendered by the encoder 100 to savecomputational complexity.

It should be noted that intra prediction is usually based on unfilteredreconstructed pixels, while inter prediction is based on filteredreconstructed pixels if these filter options are turned on by theencoder 100.

FIG. 2 is a block diagram setting forth an illustrative video decoder200 which may be used in conjunction with many video coding standards.This decoder 200 is similar to the reconstruction-related sectionresiding in the encoder 100 of FIG. 1. In the decoder 200 (FIG. 2), anincoming video bitstream 201 is first decoded through an EntropyDecoding 202 to derive quantized coefficient levels andprediction-related information. The quantized coefficient levels arethen processed through an Inverse Quantization 204 and an InverseTransform 206 to obtain a reconstructed prediction residual. A blockpredictor mechanism, implemented in an Intra/inter Mode Selector 212, isconfigured to perform either an Intra Prediction 208, or a MotionCompensation 210, based on decoded prediction information. A set ofunfiltered reconstructed pixels are obtained by summing up thereconstructed prediction residual from the Inverse Transform 206 and apredictive output generated by the block predictor mechanism, using asummer 214.

The reconstructed block may further go through an In-Loop Filter 209before it is stored in a Picture Buffer 213 which functions as areference picture store. The reconstructed video in the Picture Buffer213 can then be sent out to drive a display device, as well as used topredict future video blocks. In situations where the In-Loop Filter 209is turned on, a filtering operation is performed on these reconstructedpixels to derive a final reconstructed Video Output 222.

In video coding standards such as HEVC, blocks may be partitioned basedon quad-trees. In newer video coding standards such as the now-currentVVC, more partition methods are employed, and one coding tree unit (CTU)may be split into CUs to adapt to varying local characteristics based onquad-tree, binary-tree or ternary-tree. The separation of CU, predictionunit (PU) and transform unit (TU) does not exist in most coding modes inthe now-current VVC, and each CU is always used as the basic unit forboth prediction and transform without further partitions. However, insome specific coding modes such as intra sub-partition coding mode, eachCU may still contain multiple TUs. In the multi-type tree structure, oneCTU is firstly partitioned by a quad-tree structure. Then, eachquad-tree leaf node can be further partitioned by a binary and ternarytree structure.

FIG. 3 shows the five splitting types employed in the now-current VVC,namely, quaternary partitioning 301, horizontal binary partitioning 302,vertical binary partitioning 303, horizontal ternary partitioning 304,and vertical ternary partitioning 305. In situations where a multi-typetree structure is utilized, one CTU is first partitioned by a quad-treestructure. Then, each quad-tree leaf node can be further partitioned bya binary and ternary tree structure.

Using one or more of the exemplary block partitionings 301, 302, 303,304, or 305 of FIG. 3, spatial prediction and/or temporal prediction maybe performed using the configuration shown in FIG. 1. Spatial prediction(or “intra prediction”) uses pixels from the samples of already-codedneighboring blocks (which are called reference samples) in the samevideo picture/slice to predict the current video block. Spatialprediction reduces spatial redundancy inherent in the video signal.

In newer video coding standards such as the now-current VVC, a newcoding tool, Luma Mapping with Chroma Scaling (LMCS) has been added. TheLMCS is added as one new coding tool that is applied before the loopfilters (e.g., the de-blocking filter, the SAO and the ALF).

In general, the LMCS has two main modules: first, in-loop mapping of theluma component based on adaptive piecewise linear models; and second,luma-dependent chroma residual scaling.

FIG. 4 shows the modified decoding process with the LMCS being applied.In FIG. 4, certain blocks represent the decoding modules that areconducted in the mapped domain, which include entropy decoding 401,inverse quantization 402, inverse transform 403, luma intra prediction404 and luma sample reconstruction 405 (i.e., the addition of the lumaprediction samples Y′_(pred) and the luma residual samples Y′_(res) toproduce the reconstructed luma sample Y′_(recon)). Certain other blocksindicate the decoding modules that are conducted in the original (i.e.,non-mapped) domain, which include motion compensated prediction 409,chroma intra prediction 412, chroma sample reconstruction 413 (i.e., theaddition of the chroma prediction samples C_(pred) and the chromaresidual samples C_(res) to produce the reconstructed chroma sampleC_(recon)) and the in-loop filter process 407 (encompassing thedeblocking, the SAO and the ALF). A further group of blocks representthe new operational modules introduced by the LMCS, including forwardmapping 410 and inverse (or backward) mapping 406 of luma samples, andchroma residual scaling 411. In addition, as shown in FIG. 4, all thereference pictures that are stored in decoded picture buffer (DPB) 408(for luma) and 415 (for chroma) are in the original domain.

The in-loop mapping of the LMCS aims at adjusting the dynamic range ofthe input signal to improve the coding efficiency. The in-loop mappingof the luma samples in the existing LMCS design is built upon twomapping functions, one forward mapping function FwdMap and onecorresponding inverse mapping function InvMap. The forward mappingfunction is signaled from encoder to decoder using one piecewise linearmodel with 16 equal-size pieces. The inverse mapping function can bedirectly derived from the forward mapping function and therefore doesnot need to be signaled.

The parameters of luma mapping model are signaled at slice level. Apresence flag is firstly signaled to indicate if luma mapping model isto be signaled for a current slice. If luma mapping model is present inthe current slice, the corresponding piecewise linear model parametersare further signaled. Based on the piecewise linear model, the inputsignal's dynamic range is partitioned into 16 segments with equal sizein the original domain, and each segment is mapped to a correspondingsegment. For a given segment in the original domain, its correspondingsegment in the mapped domain may have the same or a different size. Thesize of each segment in the mapped domain is indicated by the number ofcodewords (i.e., the mapped sample values) of that segment. For eachsegment in the original domain, linear mapping parameters can be derivedbased on the number of codewords in its corresponding segment in themapped domain. For example, when the input is in 10-bit depth, each ofthe 16 segments in the original domain has 64 pixel values, if each ofthe segments in the mapped domain also has 64 codewords assigned to it,it indicates a simple one-to-one mapping (i.e. a mapping with eachsample value unchanged). The signaled number of codewords for eachsegment in the mapped domain is used to calculate the scaling factor andadjust the mapping function accordingly for that segment. Additionally,at slice level, another LMCS control flag is signaled to enable/disablethe LMCS for the slice.

For each segment, the corresponding piece-wise linear model is definedas described in the box immediately following this paragraph:

For the i-th segment, i=0 . . . 15, the corresponding piece-wise linearmodel is defined by two input pivot points InputPivot[i] andInputPivot[i+1], and two output (mapped) pivot points MappedPivot[i] andMappedPivot[i+1]. Further, assuming 10-bit input video, the values ofInputPivot[i]and MappedPivot[i], i=0 . . . 15, are calculated asfollows:

1. Set the variable OrgCW=64

2. For i=0:16, InputPivot[i]=i*OrgCW

3. For i=0:16, MappedPivot[i] is calculated as follows

MappedPivot[0]=0;

for(i=0; i<6; i++)

MappedPivot[i+1]=MaooedOuvit[i]+Sogma;edCS[i]

where SignaledCW[i] is the signaled number of codewords for the i-thsegment.

As illustrated in FIG. 4, there is a need of operating in two differentdomains during the LMCS process. For each CU coded through aninter-prediction mode (an “inter CU”), its motion compensated predictionis performed in the original domain. However, because the reconstructionof the luma component (i.e., the addition of the luma prediction samplesand the luma residual samples) is carried out in the mapped domain, themotion compensated luma prediction Y_(pred) needs to be mapped from theoriginal domain to the value Y′_(pred) in the mapped domain through theforward mapping function 410, i.e., Y′_(pred)=FwdMap(Y_(pred)), beforeY_(pred) is used for pixel reconstruction 405. On the other hand, foreach CU coded through an intra-prediction mode (an “intra CU”), themapping of the prediction samples is not needed given that the intraprediction 404 is performed in the mapped domain (as shown in FIG. 4)before Y′_(pred) is used for pixel reconstruction 405. Finally, aftergenerating the reconstructed luma samples Y′_(recon), the backwardmapping function 406 is applied to convert the reconstructed lumasamples Y′_(recon) back to a value Y_(recon) in the original domainbefore proceeding into the luma DPB 408, i.e.,Y_(recon)=InvMap(Y′_(recon)). Unlike the forward mapping 410 of theprediction samples which only needs to be applied for inter CUs, thebackward mapping 406 of the reconstructed samples needs to be applied toboth inter and intra CUs.

To sum up, at decoder side, the in-loop luma mapping of the now-currentLMCS is conducted in such a way that the luma prediction samplesY_(pred) are firstly converted to the mapped domain if needed:Y′pred=FwdMap(Ypred). Then the mapped prediction samples are added withthe decoded luma residuals to form the reconstructed luma samples in themapped domain: Y′recon=Y′pred+Y′res. Finally, the inverse mapping isapplied to convert the reconstructed luma samples Y′recon back to theoriginal domain: Yrecon=InvMap(Y′recon). At encoder side, because theluma residuals are coded in the mapped domain, they are generated as thedifference between the mapped luma original samples and the mapped lumaprediction samples: Y′res=FwdMap(Yorg)−FwdMap(Ypred).

The second step of the LMCS, luma-dependent chroma residual scaling, isdesigned to compensate for the interaction of quantization precisionbetween the luma signal and its corresponding chroma signals when thein-loop mapping is applied to the luma signal. Whether chroma residualscaling is enabled or disabled is also signaled in the slice header. Ifluma mapping is enabled and if dual-tree partition of luma and chromacomponents is disabled for the current slice, an additional flag issignaled to indicate if luma-dependent cphroma residual scaling isapplied or not. When luma mapping is not used, or when dual-treepartition is enabled for the current slice, luma-dependent chromaresidual scaling is always disabled. Additionally, chroma residualscaling is always disabled for the CUs that contain less than or equalto four chroma samples.

For both intra and inter CUs, the scaling parameters that are used toscale chroma residual are dependent on the average of the correspondingmapped luma prediction samples. The scaling parameters are derived asdescribed in the box immediately following this paragraph:

Denote avg′Y as the average of the luma prediction samples in the mappeddomain. The scaling parameter C_(ScaleInv) is computed according to thefollowing steps:

1. Find the segment index Y_(Idx) of the piecewise linear model to whichave′_(Y) belongs to the mapped domain. Here Y_(Idx) has an integer valueranging from 0 to15.

2. C_(ScaleInv)=cScaleInv[Y_(Idx)], where cScaleInv[i], i=0 . . . 15, isa pre-computed 16-piece look-up table (LUT). Because the intraprediction is performed in the mapped domain in the LMCS, for the CUsthat are coded as intra, combined intra and inter prediction (CIP), orintra block copy (IBC) modes, avg′_(Y) is cinoyted as tge average of theluma prediction samples; otherwise, avg′_(Y) is computed as the averageof the forward mapped inter predicted luma samples.

FIG. 4 also illustrates the computation of the average of lumaprediction samples for luma-dependent chroma residual scaling. For interCUs, the forward-mapped luma prediction Y′_(pred) is fed together withthe scaled chroma residuals C_(resScale) into chroma residual scaling411 to derived the chroma residuals C_(res), which is fed into chromareconstruction 413 together with chroma predictions C_(pred), in orderto derive reconstructed chroma values C_(recon). For intra CUs, intraprediction 404 produces Y′_(pred), which is already in mapped domain,and it is fed into chroma residual scaling 411 in similar fashion as forinter CUs.

Unlike the luma mapping which is performed on the sample basis,C_(ScaleInv) is fixed for the entire chroma CU. Given C_(ScaleInv),chroma residual scaling is applied as described in the box immediatelyfollowing this paragraph.

Encoder side: C_(ResScale)=C_(Res)/C_(ScaleInv)

Decoder side: C_(Res)=C_(ResScale)*C_(ScaleInv) Where C_(ResScale) andC_(Res) represent the original and the scaled chroma residual samplevalues, respectively.

In newer video coding standards such as the now-current VVC, new codingtools have been introduced, and some examples of the new coding toolsare: Bi-Directional Optical Flow (BDOF), Decoder-side Motion VectorRefinement (DMVR), Combined Inter and Intra Prediction (CIIP), AffineMode, and Prediction Refinement with Optical Flow (PROF) for affinemode.

In the now-current VVC, bi-directional optical flow (BDOF) is applied torefine the prediction samples of bi-predicted coding blocks.

FIG. 5 is an illustration of the BDOF process. The BDOF is sample-wisemotion refinement that is performed on top of the block-basedmotion-compensated predictions when bi-prediction is used. The motionrefinement (v_(x), v_(y)) of each 4×4 sub-block 501 is calculated byminimizing the difference between reference picture list 0 (L0) andreference picture list 1 (L1) prediction samples 502 and 503 after theBDOF is applied inside one 6×6 window Ω around the sub-block.

Specifically, the value of motion refinement (v_(x), v_(y)) is derivedas described in the box immediately following this paragraph.

v_(x)=S₁>0? clip3(-th_(BDOF), -((S₆.2³)≥≥└log₂S₁┘)): 0 v_(y)=S₅>0?clip3(-th_(BDOF), th_(BDOF),-((S₆.2³-((v_(x)S_(2,m))<<n_(s2)+v_(x)S_(2,s))/2) >>└log₂S₅┘)): 0

where [·] is the floor function, clip3(min, max, x) is a function thatclips a given value x inside the range of [min, max]; the symbol >>represents bitwise right shift operation; the symbol << representsbitwise lift shit operation; th_(BDOF) id the motion refinementthreshold to prevent the propagated errors due to irregular localmotion, which is equal to 1<<max(5, bitDepth-7), where bitDepth is theinternal bitdepth. Moreover, S_(2,m)=S₂>>n_(S2),S_(2.s)=S₂& (2^(n) ^(s2)−1).

The values S₁, S₂, S₃, S₅ and S₆ in the box immediately above arefurther calculated as described in the box immediately following thisparagraph.

S₁ = ∑_((i, j) ∈ Ω)ψ_(x)(i, j) ⋅ ψ_(x)(i, j), S₃ = ∑_((i, j) ∈ Ω)θ(i, j) ⋅ ψ_(x)(i, j)S₂ = ∑_((i, j) ∈ Ω)ψ_(x)(i, j) ⋅ ψ_(y)(i, j)S₅ = ∑_((i, j) ∈ Ω)ψ_(y)(i, j) ⋅ ψ_(y)(i, j)   S₆ = ∑_((i, j) ∈ Ω)θ(i, j) ⋅ ψ_(y)(i, j)where${\psi_{x}\left( {i,j} \right)} = {\left( {{\frac{\partial I^{(1)}}{\partial x}\left( {i,j} \right)} + {\frac{\partial I^{(0)}}{\partial x}\left( {i,j} \right)}} \right) ⪢ {\max\left( {1,{{bitdepth} - 11}} \right)}}$${\psi_{y}\left( {i,j} \right)} = {\left( {{\frac{\partial I^{(1)}}{\partial y}\left( {i,j} \right)} + {\frac{\partial I^{(0)}}{\partial y}\left( {i,j} \right)}} \right) ⪢ {\max\left( {1,{{bitdepth} - 11}} \right)}}$θ(i, j) = (I⁽¹⁾(i, j) ⪢ max (4, bitdepth − 8)) − (I⁽⁰⁾(i, j) ⪢ max (4, bitdepth − 8))

The values I(^(k))(i,j) in the box immediately above are the samplevalue at coordinate (i,j) of the prediction signal in list k, k=0,1,which are generated at intermediate high precision (i.e., 16-bit); andthe values

$\frac{\partial I^{(k)}}{\partial x}\left( {i,j} \right)\mspace{14mu}{and}\mspace{14mu}\frac{\partial I^{(k)}}{\partial y}\left( {i,j} \right)$

are the horizontal and vertical gradients of the sample that areobtained by directly calculating the difference between its twoneighboring sample. The values

$\frac{\partial I^{(k)}}{\partial x}\left( {i,j} \right)\mspace{14mu}{and}\mspace{14mu}\frac{\partial I^{(k)}}{\partial y}\left( {i,j} \right)$

are calculated as described in the box immediately following thisparagraph.

${\frac{\partial I^{(k)}}{\partial x}\left( {i,j} \right)} = {\left( {{I^{(k)}\left( {{i + 1},j} \right)} - {I^{(k)}\left( {{i - 1},j} \right)}} \right) ⪢ {\max\left( {6,{{bitdepth} - 6}} \right)}}$${\frac{\partial I^{(k)}}{\partial v}\left( {i,j} \right)} = {\left( {{I^{(k)}\left( {i,{j + 1}} \right)} - {I^{(k)}\left( {i,{j - 1}} \right)}} \right) ⪢ {\max\left( {6,{{bitdepth} - 6}} \right)}}$

Based on the derived motion refinement derived as described in the boximmediately following paragraph [0072] above, the final bi-predictionsamples of the CU are calculated by interpolating the L0/L1 predictionsamples along the motion trajectory based on the optical flow model, asindicated in the box immediately following this paragraph.

Based on the bit-depth control method described above, it is guaranteedthat the maximum bit-depth of the intermediate parameters of the wholeBDOF process do not exceed 32-bit and the largest input to themultiplication is within 15-bit, i.e., one 15-bit multiplier issufficient for BDOF implementations.

pred_(BDOF)(x, y) = (I⁽⁰⁾(x, y) + I⁽¹⁾(x, y) + b + o_(offset)) ⪢ shift$b = {{{rnd}\left( {\left( {v_{x}\left( {\frac{\partial{I^{(1)}\left( {x,y} \right)}}{\partial x} - \frac{\partial{I^{(0)}\left( {x,y} \right)}}{\partial x}} \right)} \right)/2} \right)} + {{rnd}\left( {\left( {v_{y}\left( {\frac{\partial{I^{(1)}\left( {x,y} \right)}}{\partial y} - \frac{\partial{I^{(0)}\left( {x,y} \right)}}{\partial y}} \right)} \right)/2} \right)}}$where  shift  and  o_(offset)  are  the  right  shift  value  and   the  offset  value  that  are  applied  to  combine  the  L 0  and  L 1  prediction  signals  for  bi-prediction, which  are  equal  to  15 − BD  and  1 ⪡ (14 − BD) + 2 ⋅ (1 ⪡ 13), respectively.

DMVR is a bi-prediction technique used for merge blocks with twoinitially signaled MVs that can be further refined by using bilateralmatching prediction.

Specifically, in DMVR, the bilateral matching is used to derive motioninformation of the current CU by finding the best match between twoblocks along the motion trajectory of the current CU in two differentreference pictures. The cost function used in the matching process isrow-subsampled SAD (sum of absolute difference). After the matchingprocess is done, the refined MVs are used for motion compensation in theprediction stage, temporal motion vector prediction for subsequentpicture and unrefined MVs are used for the motion vector predictionbetween the motion vector of the current CU and that of its spatialneighbors.

Under the assumption of continuous motion trajectory, the motion vectorsMVO and MV1 pointing to the two reference blocks shall be proportionalto the temporal distances, i.e., TD0 and TD1, between the currentpicture and the two reference pictures. As a special case, when thecurrent picture is temporally between the two reference pictures and thetemporal distance from the current picture to the two reference picturesis the same, the bilateral matching becomes mirror based bi-directionalMV.

In the now-current VVC, inter and intra prediction methods are used inthe hybrid video coding scheme, where each PU is only allowed to selectinter prediction or intra prediction for exploiting the correlation ineither temporal or spatial domain while never in both. However, aspointed out in previous literature, the residual signal generated byinter-predicted blocks and intra-predicted blocks could present verydifferent characteristics from each other. Therefore, if the two kindsof predictions can be combined in an efficient way, one more accurateprediction can be expected for reducing the energy of predictionresidual and therefore improving the coding efficiency. Additionally, innature video content, the motion of moving objects could be complicated.For example, there could exist areas which contain both old content(e.g., the objects that are included in previously coded pictures) andemerging new content (e.g., the objects that are excluded in previouslycoded pictures). In such scenario, neither inter prediction or intraprediction can provide one accurate prediction of current block.

To further improve the prediction efficiency, combined inter and intraprediction (CIIP), which combines the intra prediction and the interprediction of one CU that is coded by merge mode, is adopted in the VVCstandard. Specifically, for each merge CU, one additional flag issignaled to indicate whether the CIIP is enabled for the current CU.When the flag is equal to one, the CIIP only applies the planar mode togenerate the intra predicted samples of luma and chroma components.Additionally, equal weight (i.e., 0.5) is applied to average the interprediction samples and the intra prediction samples as the finalprediction samples of the CIIP CU.

VVC also supports Affine Mode for motion compensated prediction. InHEVC, only translation motion model is applied for motion compensatedprediction. While in the real world, there are many kinds of motion,e.g. zoom in/out, rotation, perspective motions and other irregularmotions. In the VVC, affine motion compensated prediction is applied bysignaling one flag for each inter coding block to indicate whether thetranslation motion or the affine motion model is applied for interprediction. In the now-current VVC design, two affine modes, including4-paramter affine mode and 6-parameter affine mode, are supported forone affine coding block.

The 4-parameter affine model has the following parameters: twoparameters for translation movement in horizontal and verticaldirections respectively, one parameter for zoom motion and one parameterfor rotation motion for both directions. Horizontal zoom parameter isequal to vertical zoom parameter. Horizontal rotation parameter is equalto vertical rotation parameter. To achieve a better accommodation of themotion vectors and affine parameter, in the VVC, those affine parametersare translated into two MVs (which are also called control point motionvector (CPMV)) located at the top-left corner and top-right corner of acurrent block. The affine motion field of the block is described by twocontrol point MVs (V₀, V₁).

Based on the control point motion, the motion field (v_(x), v_(y)) ofone affine coded block is calculated as described in the box immediatelyfollowing this paragraph.

$v_{x} = {{\frac{\left( {v_{1x} - v_{0x}} \right)}{w}x} - {\frac{\left( {v_{1y} - v_{0y}} \right)}{w}y} + v_{0x}}$$v_{y} = {{\frac{\left( {v_{1y} - v_{0y}} \right)}{w}x} + {\frac{\left( {v_{1x} - v_{0x}} \right)}{w}y} + v_{0y}}$

The 6-parameter affine mode has following parameters: two parameters fortranslation movement in horizontal and vertical directions respectively,one parameter for zoom motion and one parameter for rotation motion inhorizontal direction, one parameter for zoom motion and one parameterfor rotation motion in vertical direction. The 6-parameter affine motionmodel is coded with three MVs at three CPMVs.

The three control points of one 6-paramter affine block are located atthe top-left, top-right and bottom left corner of the block. The motionat top-left control point is related to translation motion, and themotion at top-right control point is related to rotation and zoom motionin horizontal direction, and the motion at bottom-left control point isrelated to rotation and zoom motion in vertical direction. Compared tothe 4-parameter affine motion model, the rotation and zoom motion inhorizontal direction of the 6-paramter may not be same as those motionin vertical direction.

Assuming (V₀, V₁, V₂) are the MVs of the top-left, top-right andbottom-left corners of the current block, the motion vector of eachsub-block (v_(x), v_(y)) is derived using three MVs at control points asdescribed in the box immediately following this paragraph.

${v_{x} = {v_{0x} + {\left( {v_{1x} - v_{0x}} \right)*\frac{x}{w}} + {\left( {v_{2x} - v_{0x}} \right)*\frac{y}{h}}}}{v_{y} = {v_{0y} + {\left( {v_{1y} - v_{0y}} \right)*\frac{x}{w}} + {\left( {v_{2y} - v_{0y}} \right)*\frac{y}{h}}}}$

To improve affine motion compensation precision, the PredictionRefinement with Optical Flow (PROF) is currently investigated in thecurrent VVC which refines the sub-block based affine motion compensationbased on the optical flow model. Specifically, after performing thesub-block-based affine motion compensation, luma prediction sample ofone affine block is modified by one sample refinement value derivedbased on the optical flow equation. In details, the operations of thePROF can be summarized as the following four steps.

In step one, the sub-block-based affine motion compensation is performedto generate sub-block prediction I(i,j) using the sub-block MVs asderived in the box immediately following paragraph [0084] above for4-parameter affine model and the box immediately following paragraph[0087] above for 6-parameter affine model.

In step two, the spatial gradients g_(x)(i,j) and g_(y)(i,j) of eachprediction samples are calculated as described in the box immediatelyfollowing this paragraph.

g_(x)(i,j)=(I(i+1,j)−I(i−1,k))>>(max (2, 14-bitdepth)−4)

g_(y)(i,j)=(I(i,j+1)−I(i,j−1)> (max (2, 14-bitdepth)−4)

Still in step two, to calculate the gradients, one additional row/columnof prediction samples need to be generated on each side of onesub-block. To reduce the memory bandwidth and complexity, the samples onthe extended borders are copied from the nearest integer pixel positionin the reference picture to avoid additional interpolation processes.

In step three, luma prediction refinement value is calculated asdescribed in the box immediately following this paragraph.

ΔI(i,j)=g_(x)(i,j)*Δv_(x)(i,j)+g_(y)(i,j)*Δv_(y)(i,j)

where the Δv(i,j) is the diference between pixel MV computed for samplelocation (i,j), noted by v(i,j), and the sub-block MV of the sub-blockwhere the pixel (i,j) locates at

Additionally, in the current PROF design, after adding the predictionrefinement to the original prediction sample, one clipping operation isperformed as the fourth step to clip the value of the refined predictionsample to be within 15-bit, as described in the box immediatelyfollowing this paragraph.

I^(r)(i,j)=I(i,j)+ΔI(i,j)

I^(r)(i,j)=clip3(−2¹⁴,2¹⁵−1, I^(r)(j,j))

where I(i,j) and I^(r)(o.k) aare tje progoma; amd refined predictionsample at location (i,j), respectively.

Because the affine model parameters and the pixel location relative tothe sub-block center are not changed from sub-block to sub-block,Δv(i,j) can be calculated for the first sub-block, and reused for othersub-blocks in the same CU. Let Δx and Δy be the horizontal and verticaloffset from the sample location (i,j) to the center of the sub-blockthat the sample belongs to, Δv(i, j) can be derived as described in thebox immediately following this paragraph.

Δv_(x)(i,j)=c*Δx+d*Δy

Δv_(y)(i,j)=e*Δx+f*Δy

Based on the affine sub-block MV derivation equations in the boxesimmediately following paragraph [0084] and paragraph [0087] above, theMV difference Δv(i, j)can be derived as described in the box immediatelyfollowing this paragraph.

${{for}\mspace{14mu} 4\text{-}{parameter}\mspace{14mu}{affine}\mspace{14mu}{model}},\left\{ {{\begin{matrix}{c = {f = \frac{v_{1x} - v_{0x}}{w}}} \\{e = {{- d} = \frac{v_{1y} - v_{0y}}{w}}}\end{matrix}{For}\mspace{14mu} 6\text{-}{parameter}\mspace{14mu}{affine}\mspace{14mu}{model}},\left\{ {{\begin{matrix}{c = \frac{v_{1x} - v_{0x}}{w}} \\{d = \frac{v_{2x} - v_{0x}}{h}} \\{e = \frac{v_{1y} - v_{0y}}{w}} \\{f = \frac{v_{2y} - v_{0y}}{h}}\end{matrix}{where}\mspace{14mu}\left( {v_{0x},v_{0y}} \right)},\left( {v_{1x},v_{1y}} \right),{\left( {v_{2x},\ v_{2y}} \right)\mspace{14mu}{are}\mspace{14mu}{the}\mspace{14mu}{top}\text{-}{{lef}t}},{{top}\text{-}{right}\mspace{14mu}{and}\mspace{14mu}{bottom}\text{-}{{lef}t}\mspace{14mu}{control}\mspace{14mu}{point}\mspace{14mu}{MVs}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{current}\mspace{14mu}{coding}\mspace{14mu}{block}},{w\mspace{14mu}{and}\mspace{14mu} h\mspace{14mu}{are}\mspace{14mu}{the}\mspace{14mu}{width}\mspace{14mu}{and}\mspace{14mu}{height}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{{block}.\mspace{14mu}{In}}\mspace{14mu}{the}\mspace{14mu}{existing}\mspace{14mu}{PROF}\mspace{14mu}{design}},{{the}\mspace{14mu}{MV}\mspace{14mu}{difference}\mspace{14mu}\Delta\; v_{x}\mspace{14mu}{and}\mspace{14mu}\Delta\; v_{y}\mspace{14mu}{are}\mspace{14mu}{always}\mspace{14mu}{derived}\mspace{14mu}{at}\mspace{14mu}{the}\mspace{14mu}{precision}\mspace{14mu}{of}\mspace{14mu}{1/32}\text{-}{{pel}.}}} \right.} \right.$

According to the now-current LMCS design, the chroma residual samplesare scaled based on their corresponding luma prediction samples. Whenthe newer coding tools are enabled for an inter CU, the luma predictionsamples used to scale the chroma residual samples through LMCS in thisinter CU are obtained at the end of the sequential applications of thesenewer coding tools.

FIG. 6 is a flow chart illustrating the workflow of the chroma residualscaling in LMCS when all of the DMVR, the BDOF and the CIIP are enabled.Outputs from Luma L0 prediction value 601 and L1 prediction value 602are fed into DMVR 603 and BDOF 604 sequentially, and the resulting lumainter prediction value 621 are fed together with the luma intraprediction value 622 from luma intra prediction 605 into average 606 toproduce the averaged luma prediction value 623, which is fed togetherwith chroma residuals 608 into chroma residual scaling 607, such thatchroma residual scaling 607, chroma prediction 610 and chromareconstruction 609 can work together to produce the final result.

The now-current LMCS design presents three challenges to the videodecoding process. First, the mappings between different domains requireextra computation complexity and on-chip memory. Second, the fact thatthe luma and chroma scaling factor derivations use different lumaprediction values introduces extra complexity. Third, the interactionbetween the LMCS and the newer coding tools introduces latency into thedecoding process.

First, in the now-current LMCS design, both the reconstructed samples inthe original domain and the mapped domain are used at various decodingmodules. As a result, these samples often need to be converted from onedomain into another between different decoding modules, which may incurboth higher computational complexity and more on-chip memory.

Specifically, for the intra mode, the CIIP mode and the IBC mode, themapped domain reference samples from the neighboring reconstructedregions of one current CU are used to generate the prediction samples.But for the inter modes, the motion compensated prediction is performedusing the original domain reconstructed samples of temporal referencepictures as references. The reconstructed samples stored in the DPB arealso in the original domain.

For example, for inter CUs, because the luma reconstruction operation(i.e. adding the prediction samples and the residual samples together)is performed in the mapped domain, the inter prediction luma samplesthat are generated in the original domain need to be converted into themapped domain before they are used for luma sample reconstruction. Inanother example, for both intra and inter CUs, the inverse (or backward)mapping is always applied to convert the reconstructed luma samples fromthe mapped domain to the original domain before storing them in the DPB.Such a design not only increases computational complexity due toadditional forward/inverse mapping operations but also requires moreon-chip memory to maintain multiple versions of the reconstructedsamples.

In practical hardware implementation, the forward and inverse (orbackward) mapping functions FwdMap and InvMap can be implemented eitherusing look-up-table (LUT) or calculated on-the-fly. When the LUT basedsolution is used, the possible output elements from functions FwdMap,InvMap and cScalelnv can be pre-calculated and pre-stored as a LUT,which can then be used for the luma mapping and chroma residual scalingoperations of all the CUs in the current slice. Assuming the input videois 10-bit, there are 2¹⁰=1024 elements in each of the LUTs for FwdMapand InvMap, and each element in the LUTs has 10-bit. Therefore, thetotal storage for the LUTs of the forward and inverse luma mapping isequal to 2*1024*10=20480 bits=2560 bytes. On the other hand, to derivethe chroma scaling parameters C_(ScaleInv), one 16-entry LUT tablecScalelnv needs to be maintained at encoder and decoder and each chromascaling parameter is stored in 32-bit. Correspondingly, the memory sizethat is used to store the LUT cScaleInv is equal to 16*32=512 bits=64bytes. The difference between 2560 and 64 shows the scale of the extraon-chip memory required by the forward and inverse (backward) mappingoperations.

Moreover, in newer video coding standards such as the now-current VVC,both the intra prediction and the deblocking filter use thereconstructed samples of above neighboring block. Therefore, one extrarow of reconstructed samples in the width of the current picture/sliceneed to be maintained in a buffer, which is also known as “line-buffer”in video coding. Reconstructed samples in the line-buffer are at leastused as references for the intra prediction and the deblockingoperations of the CUs located in the first row inside one CTU. Accordingto the existing LMCS design, the intra prediction and the deblockingfilter use the reconstructed samples in different domains. Therefore,additional on-chip memory become necessary to store both the originaland the mapped domain reconstructed samples, which could approximatelydouble the line-buffer size.

One implementation choice to avoid the doubling of line-buffer size isto perform the domain mapping operation on-the-fly. However, this comesat the expense of non-negligible computational complexity increase.

Therefore, the now-current design of the LMCS, because of the requiredmappings between different domains, will require extra computationcomplexity and on-chip memory.

Secondly, although both luma and chroma scaling factor derivationmethods in the now-current design of the LMCS use the luma predictionsample values to derive the corresponding scaling factors, there aredifferences between their corresponding operations.

For luma residual scaling, the scaling factors are derived per sample byallowing each luma residual sample to have its own scaling factor.However, for chroma residual scaling, the scaling factor is fixed forthe whole CU, i.e., all the chroma residual samples within the CU sharethe same scaling factor that is calculated based on the average of themapped luma prediction samples.

Also, two different LUTs are used to calculate the scaling factors ofluma and chroma residuals. Specifically, the input to the luma LUT isthe mapping model segment index of the original luma prediction samplevalue, while the input to the chroma LUT is the mapping model segmentindex of the average value of mapped luma prediction samples.

Such differences introduce extra complexity into the coding process, anda harmonized approach to luma and chroma scaling factor derivation isdesirable.

Thirdly, for the chroma residual scaling of the now-current design ofthe LMCS, newer coding tools, such as all the three modules of DMVR,BDOF and CIIP, can be invoked sequentially to generate the lumaprediction samples that are then used to determine the scaling factor ofthe chroma residual. Given the high computational complexity of thethree modules, to wait until their success completion before carryingout the chroma residual scaling of the LMCS could cause severe latencyfor the decoding of the chroma samples. For an affine CU, the PROFprocess may also have latency issue, as each affine CU may perform PROFprocess followed by the LMCS, which could also cause latency issue foethe decoding of the chroma samples.

The present disclosure aims at resolving or mitigating these challengespresented by the now-current design of the LMCS, more specifically, thepresent disclosure discusses schemes that may reduce the complexity ofthe LMCS for hardware codec implementation while maintaining the codinggain.

Instead of using the existing LMCS framework that converts theprediction/reconstruction samples through mapping operations, one newmethod, which is called prediction dependent residual scaling (PDRS), isproposed in this disclosure as a first aspect to scale the predictionresiduals directly without sample mapping. The proposed method canachieve similar effect and coding efficiency as LMCS, but with a muchlower implementation complexity.

According to the first aspect of the present disclosure, which is mainlyconcerned with PDRS, as illustrated in FIG. 7, a luma prediction sampleis obtained for decoding a luma residual sample (701), a scaling factoris derived using the luma prediction sample (702), the scaling factor isused to scale the luma residual sample (703), and a reconstructed lumasample is calculated by adding the luma prediction sample and the scaledluma residual sample (704).

Unlike the existing LMCS method that directly converts thepredicted/reconstructed luma samples into the mapped domain beforecalculating luma prediction residual, in the proposed method of thisfirst aspect of the present disclosure, the luma prediction residualsamples are derived in the same way as that in the regular predictionprocess in the original domain without any mapping operations, followedby a scaling operation on the luma prediction residual. The scaling ofluma prediction residual is dependent on the corresponding lumaprediction sample value and a piece-wise linear model. As a result, theforward and inverse luma mapping operations in the current LMCS designcan be completely discarded, with all the prediction and reconstructionsamples involved during the decoding process maintained in the originalsample domain. Based on the above features, the proposed method is namedPrediction Dependent Residual Scaling.

FIG. 8 is a flow chart illustrating the workflow of the decoding processwhen the first aspect of the present disclosure is applied in the LMCSprocess. It illustrates the removal of the need of mapping betweendifferent domains. Now, except for the residual decoding modules (e.g.,entropy decoding 801, inverse quantization 802 and the inverse transform803), all the other decoding modules (including intra and interprediction 804, 809, 812 and 816, reconstruction 806 and 813, and allin-loop filters 807 and 814) are operating in the original domain.Specifically, to reconstruct the luma samples, the proposed method inthe first aspect of the present disclosure only needs to de-scale theluma prediction residual samples Y_(res) back to their originalamplitude levels, then add them onto the luma prediction samplesY_(pred).

With the proposed method in the first aspect of the present disclosure,the forward and inverse luma sample mapping operations in the existingLMCS design are completely removed. This not only saves/reducescomputational complexity but also reduces the size of potential storagefor saving LMCS parameters. For instance, when the LUT-based solution isused to implement the luma mapping, the storage that is previously usedto store the two mapping LUTs FwdMap[] and InvMap[] (around 2560 Bytes)are not needed anymore in the proposed method. Furthermore, unlike theexisting luma mapping method that needs to store the reconstruction lumasamples in both the original and mapped domains, the proposed method inthe first aspect of the present disclosure generates and maintains allthe prediction and reconstruction samples only in the original domain.Correspondingly, compared to the existing luma mapping, the proposedmethod in the first aspect of the present disclosure can efficientlyreduce the line-buffer size used to store the reconstructed samples forthe intra prediction and the deblocking by half.

According to one or more embodiments of the first aspect of the presentdisclosure, the luma prediction sample and the luma residual sample arefrom one same collocated position in luma prediction block and itsassociated residual block.

According to one or more embodiments of the first aspect of the presentdisclosure, deriving the scaling factor using the luma prediction samplecomprises dividing the full range of possible luma prediction samplevalues into a plurality of luma prediction sample segments, calculatingone scaling factor for each of the plurality of the luma predictionsample segments based on a pre-defined piece linear model, anddetermining the scaling factor of the luma prediction sample based onthe scaling factors of the plurality of luma prediction sample segments.

In one example, determining the scaling factor of the luma predictionsample based on the scaling factors of the plurality of luma predictionsample segments comprises allocating the luma prediction sample into onesegment among the plurality of luma prediction sample segments andcalculating the scaling factor of the luma prediction sample as thescaling factor of the allocated luma prediction sample segment.

In this example, the plurality of luma prediction sample segmentscomprises 16 segments in a pre-defined 16-piece LUT table scaleForward,and the pre-defined piece linear model for calculating one scalingfactor for each of the plurality of the luma prediction sample segmentscomprises the 16 values corresponding to the 16 segments in thepre-defined LUT table scaleForward.

In the same example, the scaling factor is calculated based on theallocated luma prediction sample segment as described in the boxfollowing this paragraph.

Scale_(Y)=scaleForward[Idx_(Y)]

where Y is the luma residual value for which the scaling factor is beingcalculated, Scale_(Y) is the scaling factor, scaleForward[i], i=0 . . .15, is the pre-defined 16-piece LUT table, and Idx_(Y) is the segmentindex of the allocated segment for the luma prediction sample domainvalue. scaleForward[i], i=0 . . . 15 are pre-calculated as:

scaleForward[i]=(OrgCW <<SCALE_FP_PREC)/SignaledCW[i]

where OrgCW and SignaledCW[i] are the number of codewords of the i-thsegment in the original domain and the mapped domain, and SCALE_FP_PRECis the precision of scaling factor.

In the same example, given the luma scaling factor Scale_(Y), the lumaresidual sample scaling method can be applied as described in the boximmediately following this paragraph.

At encoder side: Y′_(res)=(Y_(Res)<<SCALE_FP_PREC)/Scale_(Y)

At encoder side: Y_(Res)=(Y′_(res)*Scale_(Y))>>SCALE_FP_PREC

The motivation behind this example is that, the forward mapping in thenow-current LMCS is based on one piece-wise linear model. If both theoriginal luma sample and the luma prediction sample are located at thesame piece (i.e., the same segment defined by two pivot pointsInputPivot[i] and InputPivot[i+1]), the two forward mapping functions ofthe original and prediction luma samples become exactly the same.Correspondingly, it leads toY′res=FwdMap(Yorg)-FwdMap(Ypred)=FwdMap(Yorg−Ypred)==FwdMap(Yres). Byapplying the inverse mapping on both sides of this equation, acorresponding decoder side reconstruction operation can be expressed as:Yrecon=Ypred+InvMap(Y′res).

In other words, in the situation where both the original luma sample andthe luma prediction sample are located at the same piece, the lumamapping method in LMCS can be achieved through one residual scalingoperation in the decoding process, as implemented in this possibleimplementation.

Although such a conclusion is derived based on the assumption that boththe original luma sample and the luma prediction sample are located inthe same segment defined by two pivot points InputPivot[i] andInputPivot[i+1], this possible implementation of this example can stillin any case be used as a simplification and/or approximation for theexisting luma mapping operation in VVC even when the original lumasample and the luma prediction sample are located in different segmentsof the piece-wise linear model. Experiment results show that the such asimplification and/or approximation incurs little coding performanceimpact.

To reiterate, this example is based on the assumption that both theoriginal and predicted luma sample values locate in the same segment ofthe piece-wise linear mode. In this case, the forward/inverse mappingfunctions that are applied to the original and predicted luma samplesare the same; therefore, it is safe to calculate the correspondingresidual scaling factor merely depending on the luma prediction sample.

However, when the predicted samples of the CU are not accurate enough(e.g., for intra-predicted CUs where the samples being far away from thereference samples are usually predicted less accurately), the predictionsample and the original sample are often located in different segmentsof the piece-wise linear model. In this case, the scaling factor derivedbased on the prediction sample value can be unreliable in reflecting theoriginal mapping relationship between the residual samples in theoriginal (i.e., non-mapped) domain and the residual samples in themapped domain.

FIG. 9 is an illustration of the residual mapping error caused by merelyusing the prediction sample to derive the scaling factor. In FIG. 9, thetriangle-shaped solid dots represent the pivot control points ofdifferent segments in the piece-wise linear function and thecircular-shaped solid dots represent the original and predicted samplevalues; Y_(org) and Y_(pred) are the original and predicted samples inthe original (i.e., non-mapped) domain; Y′_(org) and Y′_(pred) are themapped samples of Y_(org) and Y_(pred) respectively. Y_(res) andY′_(res) are the corresponding residuals in the original domain and themapped domain when the existing sample-based luma mapping method in VVCis applied; Y′_(resScale) is the mapped residual sample which is derivedbased on the proposed luma residual scaling scheme. As shown in FIG. 9,because the original sample and the prediction sample are not in thesame segment of the piecewise linear model, the scaling factor derivedbased on the prediction sample may not be accurate enough to produce ascaled residual (i.e., Y′_(resScale)) that approximates the originalresidual in the mapped domain (i.e., Y′_(res)).

In a second example, the assumption that both the original and predictedluma sample values locate in the same segment of the piece-wise linearmode is not required.

In this second example, instead of deriving the scaling factor directlyfrom the segment of the piece-wise linear model where the lumaprediction sample is located, the scaling factor is calculated as theaverage of the scaling factors of N (N is a positive integer number)neighboring segments.

In this second example, determining the scaling factor of the lumaprediction sample based on the scaling factors of the plurality of lumaprediction sample segments comprises allocating the luma predictionsample into one segment among the plurality of luma prediction samplesegments and calculating the scaling factor of the luma predictionsample as the average of the scaling factors of a number of lumaprediction sample segments that are neighboring to the allocated lumaprediction sample segment.

More specifically, in one possible implementation of this secondexample, the scaling factor may be calculated based on the allocatedluma prediction sample segment as described in the following steps.

1) Finding or obtaining the corresponding segment index Idx_(Y) of thepiece-wise linear model which the Pred_(Y) belongs to in the originaldomain.

If Y′_(res)≥0, the luma residual scaling factor is calculated as:

${{Scal}e_{Y}} = {\frac{1}{N}{\sum\limits_{i = {Idx}_{Y}}^{{{Id}x_{Y}} + N - 1}{{scaleForward}\lbrack i\rbrack}}}$

3) Otherwise (i.e., Y′_(res)<0), the luma residual scaling factor iscalculated as:

${{Scal}e_{Y}} = {\frac{1}{N}{\sum_{i = {Idx}_{Y - N + 1}}^{{Id}x_{Y}}{{{scaleForward}\lbrack i\rbrack}.}}}$

where scaleForward[i], i=0 . . . 15, is the pre-defined 16-piece LUT,which is calculated as:

scaleForward[i]=(OrgCW <<SCALE_FP_PREC)/SignaledCW[i]

where OrgCW and SignaledCW[i] are the number of codewords of the i-thsegment in the original domain and the mapped domain respectively, andSCALE_FP_PREC is the precision of scaling factor.

In a second possible implementation of this second example that isotherwise identical to the implementation described above, the scalingfactor may be calculated based on allocated luma prediction samplesegment as described in the box immediately following this paragraph:

1) Finding or obtaining the corresponding segment index Idx_(Y) of thepiece-wise linear model which the Pred_(Y) belongs to in the originaldomain.

2) The luma residual scaling factor is calculated as:

${{Scal}e_{Y}} = {\frac{1}{N}{\sum\limits_{i = {{Idx}_{Y} - M}}^{{{Id}x_{Y}} + N - 1 - M}{{scaleForward}\lbrack i\rbrack}}}$

Where scaleForward remains the same as in the previous example, and M isan integer number in the range of [0, (N−1)]. One exemplar value of M is(N−1)/2. Another exemplar value of M may be N/2.

The above two possible implementations of this second example onlydiffer in the selection of the N luma prediction sample domain valuesegments based on the allocated segment.

According to a second aspect of the present disclosure, as illustratedin FIG. 10, a luma prediction sample value is obtained for decoding botha luma residual sample and a chroma residual sample at an input position(1001), a luma prediction sample associated with the luma residualsample is then obtained (1002), a chroma prediction sample associatedwith the chroma residual sample is then obtained (1003), the lumaprediction sample is used to derive a first scaling factor for the lumaresidual sample and a second scaling factor for the chroma residualsample (1004), the first scaling factor is used to scale the lumaresidual sample (1005), the second scaling factor is used to scale thechroma residual sample (1006), a reconstructed luma sample is calculatedby adding the luma prediction sample and the scaled luma residual sample(1007), and a reconstructed chroma sample is calculated by adding thechroma prediction sample and the scaled chroma residual sample (1008).

This second aspect of the present disclosure aims at harmonizing thescaling methods of luma and chroma residuals so as to achieve a moreunified design.

According to one or more embodiments of the second aspect of the presentdisclosure, the luma prediction sample value is an average of all lumaprediction samples in a coding unit (CU) containing the input position.In these embodiments, the chroma scaling derivation method is used tocalculate the scaling factor for luma residuals, more specifically,instead of separately deriving one scaling factor for each luma residualsample, one shared scaling factor which is calculated based on theaverage of luma prediction samples is used to scale the luma residualsamples of the whole CU.

According to another embodiment of the second aspect of the presentdisclosure, the luma prediction sample value is an average of all lumaprediction samples in a pre-defined subblock sub-divided from a codingunit (CU) containing the input position. In this embodiment, one CU isfirstly equally partitioned into multiple M x N subblocks; then for eachsubblock, all or partial luma prediction samples are used to derive acorresponding scaling factor that is used to scale both the luma andchroma residuals of the subblock. Compared to the first method, thesecond method can improve the spatial precision of the estimated scalingfactor because the less correlated luma prediction samples that areoutside a subblock are excluded from calculating the scaling factor ofthe subblock. Meanwhile, the second method can also reduce the latencyof luma and chroma residual reconstruction, given that the scaling ofluma and chroma residuals in one subblock can be immediately startedafter the luma prediction of the subblock is finished, i.e., withoutwaiting for the full generation of the luma prediction samples of thewhole CU.

According to a third embodiment of the second aspect of the presentdisclosure, the luma prediction sample domain value comprises acollocated luma prediction sample. In this embodiment, the luma residualscaling method is extended to scaling the chroma residuals, anddifferent scaling factors for each chroma residual sample are derivedbased on its collocated luma prediction sample value.

In the above embodiments of the second aspect of the present disclosure,it is proposed to use the same LUT that is used for calculating the lumascaling factor to do the scaling of chroma residuals. In one example, toderive a CU-level scaling factor ScaleC for chroma residual, thefollowing may be followed:

1) Calculating the average of the luma prediction samples (which arerepresented in the original domain) within the CU, denoted as avg_(Y).

2) Finding or obtaining the corresponding segment index Idx_(Y) of thepiece-wise linear model which the avg_(Y) belongs to.

3) Calculating the value of Scale_(C) as:

Scale_(C)=scaleForward[Idx_(Y)]

where scaleForward[i], i=0 . . . 15, is one pre-defined 16-piece LUT,which is calculated as:

scaleForward[i]=(OrgCW <<SCALE_FP_PREC)/SignaledCW[i]

where OrgCW and SignaledCW[i] are the number of codewords of the i-thsegment in the original domain and the mapped domain respectively, andSCALE_FP_PREC is the precision of scaling factor.

The example above can be easily extended to the case where a scalingfactor for chroma residual is derived per each subblock of a current CU.In that case, in the first step above avgY would be calculated as theaverage of the luma prediction samples in the original domain of asubblock, while step 2 and step 3 remain the same.

According to a third aspect of the present disclosure, as illustrated inFIG. 11, a plurality of luma prediction samples is obtained by skippinga number of a pre-defined intermediate luma prediction stages during aluma prediction process for a coding unit (CU) (1101), the obtainedplurality of luma prediction samples is used to derive scaling factorsfor chroma residual samples in the CU (1102), the scaling factors areused to scale the chroma residual samples in the CU (1103), and areconstructed chroma sample is calculated by adding the chromaprediction samples and the scaled chroma residual samples in the CU(1104).

According to one or more embodiments of the third aspect of the presentdisclosure, the pre-defined intermediate luma prediction stages containone or more bi-prediction modules of Decoder-side Motion VectorDerivation (DMVR), Bi-Directional Optical Flow (BDOF) and Combined Interand Intra Prediction (CIIP). In these embodiments, the inter predictionsamples derived before the DMVR, the BDOF/PROF, the CIIP intra/intercombination process are used to derive the scaling factor for the chromaresiduals.

FIG. 12 is a flow chart illustrating the workflow of the LMCS decodingprocess in one example of this embodiment of the third aspect of thepresent disclosure where the DMVR, the BDOF and the CIIP are not appliedto generate the luma prediction samples for the chroma scaling. Here,instead of waiting for the DMVR 1203, the BDOF 1204 and/or the CIIP'sluma intra prediction part 1205 to be fully finished, the chromaresidual scaling process 1208 can be started as soon as the predictionsamples 1221 and 1222 based on the initial L0 and L1 luma prediction1201 and 1202 become available.

In FIG. 12, one additional averaging operation 1211 in addition to theoriginal averaging operation 1206 is needed to combine the initial L0and L1 prediction samples 1221 and 1222 prior to DMVR 1203, BDOF 1204,and/or CIIP 1205.

To reduce the complexity, in a second example of this embodiment of thethird aspect of the present disclosure, the initial L0 predictionsamples may be used to derive the scaling factor for chroma residuals.

FIG. 13 is a flow chart illustrating the workflow of the LMCS decodingprocess in the second example of this embodiment of the third aspect ofthe present disclosure where the initial uni-prediction signal isapplied to generate the luma prediction samples for the chroma scaling.No additional averaging operation in addition to the original averagingoperation 1306 is needed. The initial LO prediction samples 1321 areused to derive the scaling factor for chroma residuals prior to DMVR1303, BDOF 1304, and/or CIIP 1305.

In a third example of this embodiment of the third aspect of the presentdisclosure, one initial prediction signal (L0 or L1) is chosen in anadaptive manner as the luma prediction samples that are used forderiving the chroma residual scaling factor. In one possibleimplementation of this example, between the initial prediction signal(L0 or L1), the one whose reference picture has a smaller picture ordercount (POC) distance relative to the current picture is selected forderiving the chroma residual scaling factor.

In another embodiment of the third aspect of the present disclosure, itis proposed to only disable the DMVR, the BDOF/PROF while enabling theCIIP for generating the inter prediction samples that are used fordetermining chroma residual scaling factor. Specifically, in thismethod, the inter prediction samples derived before the DMVR and theBDOF/PROF are firstly averaged which are then combined with the intraprediction samples for the CIIP; finally, the combined predictionsamples are used as the prediction samples for deciding the chromaresidual scaling factor.

In yet another embodiment of the third aspect of the present disclosure,it is proposed to only disable the BDOF/PROF while keeping the DMVR andthe CIIP for generating the prediction samples that are used fordetermining chroma residual scaling factor.

In still another embodiment of the third aspect of the presentdisclosure, it is proposed to keep the BDOF/PROF and the CIIP whiledisabling the DMVR in deriving the luma prediction samples that are usedfor determining chroma residual scaling factor.

Moreover, it is worth mentioning that although the methods in theembodiments above of the third aspect of the present disclosure areillustrated as they are designed for reducing the latency of chromaprediction residual scaling, those methods can also be used for reducingthe latency of luma prediction residual scaling. For example, all thosemethods can also be applied to the PDRS method explained in the section“luma mapping based on prediction-dependent residual scaling”.

According to the existing DMVR design, in order to save computationalcomplexity, the prediction samples used for the DMVR motion refinementare generated using 2-tap bilinear filters instead of default 8-tapinterpolation. After the refined motion are determined, the default8-tap filters will be applied to generate the final prediction samplesof the current CU. Therefore, to reduce the chroma residual decodinglatency caused by the DMVR, it is proposed to use the luma predictionsamples (the average of L0 and L1 prediction samples if the current CUis bi-predicted) that are generated by the bilinear filters to determinethe scaling factor of chroma residuals.

According to a fourth aspect of the present application, as illustratedin FIG. 14, one or more luma prediction sample values are selected froman output of a bilinear filter of Decoder-side Motion Vector Derivation(DMVR) (1401), the one or more selected luma prediction sample valuesare adjusted into luma prediction sample values with the same bit depthas an original coding bit depth of an input video (1402), the lumaprediction sample values with the same bit depth as the original codingbit depth of the input video are used to derive a scaling factor fordecoding one or more chroma residual samples (1403), the scaling factoris used to scale one or more chroma residual samples (1404), and one ormore chroma residual samples are reconstructed by adding the one or morescaled chroma residual samples and their corresponding chroma predictionsamples (1405).

In one or more embodiments of the fourth aspect of the presentdisclosure, selecting the one or more luma prediction sample values fromthe output of the bilinear filter of DMVR comprises selecting L0 and L1luma prediction samples from the output of the bilinear filter of DMVR.

FIG. 15 is a flow chart illustrating the workflow of the LMCS decodingprocess in one such embodiment of the fourth aspect of the presentdisclosure. L0 and L1 prediction samples 1521 and 1522 from the outputof bilinear filter 1512 component of DMVR 1503 are fed into average 1511in order to derive a chroma residual scaling input 1523 to be used inchroma residual scaling 1507 for decoding one or more chroma residualsamples.

In these embodiments, there is an issue of bit code depth. In order tosave the internal storage size used by the DMVR, the intermediate L0 andL1 prediction samples generated by the bilinear filters of the DMVR arein 10-bit precision. This is different from the representation bit-depthof the immediate prediction samples of regular bi-prediction, which isequal to 14-bit. Therefore, the intermediate prediction samples outputfrom the bilinear filters cannot be directly applied to determine thechroma residual scaling factor due to its different precision.

To deal with this issue, it is proposed to firstly align the DMVRintermediate bit-depth with the intermediate bi-depth used for regularmotion compensated interpolation, i.e., increase the bit-depth from10-bit to 14-bit. After that, the existing average process that isapplied to generate regular bi-prediction signal can be reused togenerate the corresponding prediction samples for the determination ofchroma residual scaling factor.

In one example of these embodiments, adjusting the one or more selectedluma prediction sample values into the another or more luma predictionsample values with the same bit depth as the original coding bit depthof the input video comprises increasing an internal bit depth of the L0and L1 luma prediction samples from the output of the bilinear filter ofDMVR to 14-bit through left shifting, obtaining a 14-bit average lumaprediction sample value by averaging the 14-bit shifted L0 and L1 lumaprediction sample values, and converting the 14-bit average lumaprediction sample values by changing the internal bit depth of the14-bit average luma prediction sample values to the original coding bitdepth of the input video through right shifting.

More specifically, in this example, the chroma scaling factor isdetermined by the steps described in the box immediately following thisparagraph.

1) Internal bit-depth alignment: increase the internal bit-depth of theL0 and L1 prediction samples generated by the bilinear filters from10-bit to 14-bit, as illustrated as

P₀ ^(scake)(i,j)=(P₀(i,j) <<4)−2¹³P₁ ^(scale)(i,j)=(P₁(i,j) <<4)−2¹³

where P₀(i,j) and P₁(i,j) are the prediction samples output from thebilinear filters and P₀ ^(scale)(i,j) and P₁ ^(scale)(i,j) are thescaled prediction samples after bit-depth alignment; 2¹³ is the constantnumber that is used to compensate the shifted dynmanic range ofprediction samples that is caused by the following average operation.

2) Average of L0 and L1 scaled prediction samples: the final lumasamples that arw used to determine the chroma residual scaling factorare calculated by averaging the two scaled luma prediction samples as

P_(ave)(i,j)=((P₀ ^(scale)(i,j)+P₁ ^(scale)(i,j)+2·2¹³ +2^(14-bitdepth))>>(14-bitdepth+1)

where bitdepth is the coding bit-depth of the input video.

In other embodiments of the fourth aspect of the present disclosure,selecting the one or more luma prediction sample values from the outputof the bilinear filter of DMVR and adjusting the one or more selectedluma prediction sample values into the another or more luma predictionsample values with the same bit depth as the original coding bit depthof the input video comprise selecting one luma predication sample out ofL0 and L1 luma prediction samples from the output of the bilinear filterof DMVR, adjusting the one selected luma prediction sample by changingan internal bit depth of the one selected luma prediction value to theoriginal coding bit depth of the input video through shifting, and usingthe adjusted luma prediction sample as the luma prediction sample withthe same bit depth as the original coding bit depth of the input video.

FIG. 16 is a flow chart illustrating the workflow of the LMCS decodingprocess in one such other embodiment of the fourth aspect of the presentdisclosure. L0 prediction samples 1621 from the output of bilinearfilter 1612 component of DMVR 1603 is used in chroma residual scaling1607 for decoding one or more chroma residual samples.

In one example of one such other embodiment of the fourth aspect of thepresent disclosure, the chroma scaling factor is determined by shiftingthe luma samples output from bilinear filters to the original codingbit-depth of the input video as described in the box immediatelyfollowing this paragraph.

If bitdepth is no larger than 10, then

P_(ave)(i,j)=P₀(i,j) >>(10bitdepth)

Otherwise,

P_(ave)(i,j)=P₀(i,j) <<(bitdepth-10)

According to a fifth aspect of the present application, as illustratedin FIG. 17, one or more luma reference sample values are selected fromreference pictures (1701), the one or more selected luma referencesample values are transformed into a luma sample value (1702), the lumasample value is used to derive a scaling factor (1703), the scalingfactor is used to scale one or more chroma residual samples (1704), andone or more chroma residual samples are reconstructed by adding the oneor more scaled chroma residual samples and their corresponding chromaprediction samples (1705).

In one or more embodiments of the fifth aspect of the presentdisclosure, selecting the one or more luma reference sample values fromthe reference pictures and transforming the one or more selected lumareference sample values into the luma sample value comprise obtainingboth L0 and L1 luma reference sample values from L0 and L1 referencepictures and averaging the L0 and L1 luma reference sample values as theluma sample value.

In other embodiments of the fifth aspect of the present disclosure,selecting the one or more luma reference samples from the referencepictures and transforming the one or more selected luma referencesamples into the luma sample value comprise selecting one luma referencesample value out of L0 and L1 luma reference sample values from L0 andL1 reference pictures and using the one selected luma reference samplevalues as the luma sample value.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof If implementedin software, the functions may be stored on or transmitted over, as oneor more instructions or code, a computer-readable medium and executed bya hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother, e.g., according to a communication protocol. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the implementationsdescribed in the present application. A computer program product mayinclude a computer-readable medium.

Further, the above methods may be implemented using an apparatus thatincludes one or more circuitries, which include application specificintegrated circuits (ASICs), digital signal processors (DSPs), digitalsignal processing devices (DSPDs), programmable logic devices (PLDs),field programmable gate arrays (FPGAs), controllers, micro-controllers,microprocessors, or other electronic components. The apparatus may usethe circuitries in combination with the other hardware or softwarecomponents for performing the above described methods. Each module,sub-module, unit, or sub-unit disclosed above may be implemented atleast partially using the one or more circuitries.

The present disclosure may include dedicated hardware implementationssuch as application specific integrated circuits, programmable logicarrays and other hardware devices. The hardware implementations can beconstructed to implement one or more of the methods described herein.Examples that may include the apparatus and systems of variousimplementations can broadly include a variety of electronic andcomputing systems. One or more examples described herein may implementfunctions using two or more specific interconnected hardware modules ordevices with related control and data signals that can be communicatedbetween and through the modules, or as portions of anapplication-specific integrated circuit. Accordingly, the apparatus orsystem disclosed may encompass software, firmware, and hardwareimplementations. The terms “module,” “sub-module,” “circuit,”“sub-circuit,” “circuitry,” “sub-circuitry,” “unit,” or “sub-unit” mayinclude memory (shared, dedicated, or group) that stores code orinstructions that can be executed by one or more processors. The modulerefers herein may include one or more circuit with or without storedcode or instructions. The module or circuit may include one or morecomponents that are connected.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed here. This application is intended to cover anyvariations, uses, or adaptations of the invention following the generalprinciples thereof and including such departures from the presentdisclosure as come within known or customary practice in the art. It isintended that the specification and examples be considered as exemplaryonly, with a true scope and spirit of the invention being indicated bythe following claims.

It will be appreciated that the present invention is not limited to theexact examples described above and illustrated in the accompanyingdrawings, and that various modifications and changes can be made withoutdeparting from the scope thereof. It is intended that the scope of theinvention only be limited by the appended claims.

We claim:
 1. A method for video coding, comprising: selecting one ormore luma prediction sample values from an output of a bilinear filterof Decoder-side Motion Vector Derivation (DMVR); adjusting the one ormore selected luma prediction sample values into luma prediction samplevalues with the same bit depth as an original coding bit depth of aninput video; using the luma prediction sample values with the same bitdepth as the original coding bit depth of the input video to derive ascaling factor for decoding one or more chroma residual samples; usingthe scaling factor to scale one or more chroma residual samples; andreconstructing one or more chroma residual samples by adding the one ormore scaled chroma residual samples and their corresponding chromaprediction samples.
 2. The method of claim 1, wherein selecting the oneor more luma prediction sample values from the output of the bilinearfilter of DMVR comprises selecting L0 and L1 luma prediction samplesfrom the output of the bilinear filter of DMVR.
 3. The method of claim2, wherein adjusting the one or more selected luma prediction samplevalues into the luma prediction sample values with the same bit depth asthe original coding bit depth of the input video comprises: increasingan internal bit depth of the L0 and L1 luma prediction samples from theoutput of the bilinear filter of DMVR to 14-bit through left shifting;obtaining a 14-bit average luma prediction sample value by averaging the14-bit shifted L0 and L1 luma prediction sample values; and convertingthe 14-bit average luma prediction sample values by changing theinternal bit depth of the 14-bit average luma prediction sample valuesto the original coding bit depth of the input video through rightshifting.
 4. The method of claim 1, wherein selecting the one or moreluma prediction sample values from the output of the bilinear filter ofDMVR and adjusting the one or more selected luma prediction samplevalues into the luma prediction sample values with the same bit depth asthe original coding bit depth of the input video comprise: selecting oneluma predication sample out of L0 and L1 luma prediction samples fromthe output of the bilinear filter of DMVR; adjusting the one selectedluma prediction sample by changing an internal bit depth of the oneselected luma prediction value to the original coding bit depth of theinput video through shifting; and using the adjusted luma predictionsample as the luma prediction sample with the same bit depth as theoriginal coding bit depth of the input video.
 5. A computing devicecomprising: one or more processors; a non-transitory storage coupled tothe one or more processors; and a plurality of programs stored in thenon-transitory storage that, when executed by the one or moreprocessors, cause the computing device to perform acts comprising:selecting one or more luma prediction sample values from an output of abilinear filter of Decoder-side Motion Vector Derivation (DMVR);adjusting the one or more selected luma prediction sample values intoluma prediction sample values with the same bit depth as an originalcoding bit depth of an input video; using the luma prediction samplevalues with the same bit depth as the original coding bit depth of theinput video to derive a scaling factor for decoding one or more chromaresidual samples; using the scaling factor to scale one or more chromaresidual samples; and reconstructing one or more chroma residual samplesby adding the one or more scaled chroma residual samples and theircorresponding chroma prediction samples.
 6. The computing device ofclaim 5, wherein selecting the one or more luma prediction sample valuesfrom the output of the bilinear filter of DMVR comprises selecting L0and L1 luma prediction samples from the output of the bilinear filter ofDMVR.
 7. The computing device of claim 6, wherein adjusting the one ormore selected luma prediction sample values into the luma predictionsample values with the same bit depth as the original coding bit depthof the input video comprises: increasing an internal bit depth of the L0and L1 luma prediction samples from the output of the bilinear filter ofDMVR to 14-bit through left shifting; obtaining a 14-bit average lumaprediction sample value by averaging the 14-bit shifted L0 and L1 lumaprediction sample values; and converting the 14-bit average lumaprediction sample values by changing the internal bit depth of the14-bit average luma prediction sample values to the original coding bitdepth of the input video through right shifting.
 8. The computing deviceof claim 5, wherein selecting the one or more luma prediction samplevalues from the output of the bilinear filter of DMVR and adjusting theone or more selected luma prediction sample values into the lumaprediction sample values with the same bit depth as the original codingbit depth of the input video comprise: selecting one luma predicationsample out of LO and L1 luma prediction samples from the output of thebilinear filter of DMVR; adjusting the one selected luma predictionsample by changing an internal bit depth of the one selected lumaprediction value to the original coding bit depth of the input videothrough shifting; and using the adjusted luma prediction sample as theluma prediction sample with the same bit depth as the original codingbit depth of the input video.
 9. A non-transitory computer readablestorage medium storing a plurality of programs for execution by acomputing device having one or more processors, wherein the plurality ofprograms, when executed by the one or more processors, cause thecomputing device to perform acts comprising: selecting one or more lumaprediction sample values from an output of a bilinear filter ofDecoder-side Motion Vector Derivation (DMVR); adjusting the one or moreselected luma prediction sample values into luma prediction samplevalues with the same bit depth as an original coding bit depth of aninput video; using the luma prediction sample values with the same bitdepth as the original coding bit depth of the input video to derive ascaling factor for decoding one or more chroma residual samples; usingthe scaling factor to scale one or more chroma residual samples; andreconstructing one or more chroma residual samples by adding the one ormore scaled chroma residual samples and their corresponding chromaprediction samples.
 10. The non-transitory computer readable storagemedium of claim 9, wherein selecting the one or more luma predictionsample values from the output of the bilinear filter of DMVR comprisesselecting L0 and L1 luma prediction samples from the output of thebilinear filter of DMVR.
 11. The non-transitory computer readablestorage medium of claim 10, wherein adjusting the one or more selectedluma prediction sample values into the luma prediction sample valueswith the same bit depth as the original coding bit depth of the inputvideo comprises: increasing an internal bit depth of the L0 and L1 lumaprediction samples from the output of the bilinear filter of DMVR to14-bit through left shifting; obtaining a 14-bit average luma predictionsample value by averaging the 14-bit shifted L0 and L1 luma predictionsample values; and converting the 14-bit average luma prediction samplevalues by changing the internal bit depth of the 14-bit average lumaprediction sample values to the original coding bit depth of the inputvideo through right shifting.
 12. The non-transitory computer readablestorage medium of claim 9, wherein selecting the one or more lumaprediction sample values from the output of the bilinear filter of DMVRand adjusting the one or more selected luma prediction sample valuesinto the luma prediction sample values with the same bit depth as theoriginal coding bit depth of the input video comprise: selecting oneluma predication sample out of L0 and L1 luma prediction samples fromthe output of the bilinear filter of DMVR; adjusting the one selectedluma prediction sample by changing an internal bit depth of the oneselected luma prediction value to the original coding bit depth of theinput video through shifting; and using the adjusted luma predictionsample as the luma prediction sample with the same bit depth as theoriginal coding bit depth of the input video.